Inclusion of potential vorticity uncertainties into a hydrometeorological forecasting chain:

Application to a flash-flood event over Catalonia, Spain

A. Amengual, R. Romero, M. Vich and S. Alonso

Grup de Meteorologia, Departament de Física, Universitat de les Illes Balears,

Palma, Mallorca, Spaine-mail: arnau.amengual@uib.es

Universitat de les Illes Balears

Inclusion of potential vorticity uncertainties into a hydrometeorological forecasting chain: Application

to a flash-flood event over Catalonia, Spain

1. General aspects of flood events in the Western Mediterranean area

2. Hydrometeorological forecasting chain

3. The “Montserrat” flash-flood event

4. Hydrological and meteorological tools

5. Probabilistic versus deterministic QPFs: Inclusion of Potential Vorticity uncertainties

6. Probabilistic versus deterministic QPFs and driven runoff simulations

7. Conclusions and further remarks

Floods are hydrometeorological events:depend on both hydrological and meteorological factors

1. Meteorological conditions:

1.1 Incursion of cold air masses: increase in the probability of heavy precipitations

1.2 Closed and warm sea:

- surrounded by coastal high mountains- extreme rainfalls favoured by high SST

• Convective instabilities along the cold fronts producing interactions between frontal and orographic enhancement

1. General aspects of flood events in the Western Mediterranean area

2. Hydrological conditions:

2.1 antecedent moisture of the soil: infiltration properties

2.2 terrain and surface runoff characteristics: mountain systems and high urbanization rates near the coast

• Flash-floods are often associated with quasi-stationary convective events: high precipitation rates + periods of several hours

• These are experienced in urban areas frequently in time and randomly in space

• Short temporal scale of these episodes (~ hours): occurrence too rapid to attempt damage mitigation

• Flash-floods are dangerous in terms of human lives and properties

1. General aspects of flood events in the Western Mediterranean area

• Hydrometeorological modeling chain: spatial and temporal scales of the numerical weather predictions

GCMs / NWP (~103-104 km2, several days) LAMs / NWP (~10 km2, 48 h) HMS

• Mesoscale models provide realistic rainfall distributions for heavy precipitation episodes • Supply a useful support for flood simulation and forecasting: further extension lead times • Coupled atmospheric-hydrologic system: advanced validation tool for the simulated rainfall amounts

2. Hydrometeorological forecasting chain

Sources of uncertainty and limitations

• Errors in initial and boundary conditions

• Approximations in physical parameterizations

• Models’ structures

impacts on the hydrometeorological simulations

2. Hydrometeorological forecasting chain

3. The “Montserrat” flash-flood event

Synoptic situation

1. Entrance of an Atlantic low-level cold front and an upper-level trough2. Generation of amesoscale cyclone in theMediterranean Sea 3. Advection of warm andmoist air toward Catalonia from the Mediterranean

Easterly flow + Atlantic front +orographic enhancement

quasi-stationary convectivesystem

9 June 2000 00 UTC 10 June 2000 00 UTC

• Torrential precipitation took place on 10 June 2000 from 00 to 06 UTC: hourly quantities above 100 mm and 6 h maximum up to 224 mm

• The Llobregat catchment is a medium-size basin with an area of 5040 km2 and a length close to 170 km. Accumulated rainfall reached over 200 mm inside this basin

3. The “Montserrat” flash-flood event

3. The “Montserrat” flash-flood event

• Huge increase in the river flow, producing 5 fatalities, 500 evacuated people and material damage estimated at over 65 M€

• Return period: Q= 1550 m3s-1 25 yrs

4. Hydrological and meteorological tools

MM5 model set-up

• Meteorological simulations have used the same model configuration as in the real-time operational version at UIB (http://mm5forecasts.uib.es)

• Initial and boundary conditions: ECMWF forecasts (update 6h, 0.3º)

• Two domains: 22.5 and 7.5 km, interacting with each other and 30 vertical σ-levels

• Kain-Fritsch scheme is used to parameterise convection for both domains

• The experiments consider a 54h period simulation (09/06/00 00 UTC - 11/06/00 06 UTC)

HEC-HMS model set-up

• Loss rate: Soil Conservation Service Curve Number (SCS-CN) model

• Transform: SCS Unit Hydrograph model

• Flow routing: Muskingum method

• The experiments consider a 96h period simulation (09/06/2000 00 UTC-13/06/2000 00 UTC)

5. Probabilistic versus deterministic QPFs

Spatial distribution of accumulated precipitation for the control simulation

Difficulties to correctly forecast precise location and timing of convectively-driven rainfall system affecting small and medium size basins

-99.7

1.1

EV (%)

-99.7-0.05control

-7.80.84rain-gauges

EP (%)NSEMontserrat event

5. Probabilistic versus deterministic QPFs: Inclusion of Potential Vorticity uncertainties

Initial conditions of the upper-level precursor trough

Slight perturbations on the initial state: study of the spatial and temporal uncertainties of QPFs into a medium-sized catchment

• To introduce realistic perturbations in the ensemble prediction system (EPS), a PV error climatology (PVEC) has been carried out. This allows to perturb the PV

fields using the appropriated error range

• The PVEC is calculated using a large collection of MEDEX cases (19 cases, 56 days of simulation; further information available at: http://medex.inm.uib.es/), and provides displacement and intensity errors of the PV fields in the study region

• The displacement error (DE) corresponds to the displacement of the ECMWF 24 h forecast PV field showing local maximum correlation with the ECMWF analyses PV field

Percentile levels of displacement

errors at 300 hPa

5. Probabilistic versus deterministic QPFs: Inclusion of Potential Vorticity uncertainties

• Intensity error (IE) corresponds to the difference between the displaced ECMWF 24 h forecast and analyses PV fields

• PVEC is used to implement the EPS by randomly perturbing the PV field

• Perturbations are applied along the areas with the most intense PV values and gradients

• PV Inversion Technique consistently perturb the mass and wind fields, but conserving the energetic balance

Percentile levels of intensity errors at 300 hPa

5. Probabilistic versus deterministic QPFs: Inclusion of Potential Vorticity uncertainties

Ensemble mean (in mm, shaded) and standard deviation (in mm, continuous line at 5 mm interval)

• MM5 ensemble comprises 21 elements (control + 20 PV-perturbed)

• Important spread on rainfall values

• Highly sensitive to PV perturbations

• Essential role of atmospheric dynamical forcing

5. Probabilistic versus deterministic QPFs

6. Probabilistic versus deterministic QPFs driven runoff simulations

• Elements of the driven runoff ensemble are considered equally-like

• Cumulative distribution functions (CDFs) of driven runoff peak flows

7. Conclusions and further remarks

• MM5 control simulation is deficient for the Montserrat event: maximum precipitation amounts are obtained quite far away from the Llobregat basin

• PQPFs reduce biases obtained for the deterministic forecast

• For civil protection purposes, a hypothetical first warning for a peak flow exceeding Qp (T = 5 yrs) would have produced a probability of exceedence over 0.25. This fact points out the benefits of a probabilistic versus a deterministic prediction system • The performance of the hydrometeorological simulations strongly depends on the initial and boundary conditions of the databases and on the case under study

• References:

Amengual et al. (2009): Inclusion of potential vorticity uncertainties into a hydro-meteorological forecasting chain: Application to a medium size basin of Mediterranean Spain. Hydrol. Earth Syst. Sci., 13, 793-811